Overview

Dataset statistics

Number of variables12
Number of observations5706
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory579.5 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

recency is highly overall correlated with purchase_countHigh correlation
avg_purchase_interval is highly overall correlated with purchase_countHigh correlation
nunique_products is highly overall correlated with avg_basket_size and 2 other fieldsHigh correlation
avg_basket_size is highly overall correlated with nunique_products and 2 other fieldsHigh correlation
purchase_count is highly overall correlated with recency and 4 other fieldsHigh correlation
charge_back_count is highly overall correlated with purchase_count and 1 other fieldsHigh correlation
return_rate is highly overall correlated with purchase_count and 1 other fieldsHigh correlation
avg_order_value is highly overall correlated with nunique_products and 2 other fieldsHigh correlation
gross_revenue is highly overall correlated with nunique_products and 3 other fieldsHigh correlation
avg_unt_price is highly skewed (γ1 = 40.05551367)Skewed
avg_order_value is highly skewed (γ1 = 21.75818136)Skewed
gross_revenue is highly skewed (γ1 = 23.73500315)Skewed
customer_id has unique valuesUnique
charge_back_count has 4199 (73.6%) zerosZeros
return_rate has 4199 (73.6%) zerosZeros

Reproduction

Analysis started2023-05-29 19:33:07.911097
Analysis finished2023-05-29 19:33:31.906088
Duration23.99 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct5706
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11208.74
Minimum-3710
Maximum18287
Zeros0
Zeros (%)0.0%
Negative1371
Negative (%)24.0%
Memory size89.2 KiB
2023-05-29T16:33:32.046976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3710
5-th percentile-2747
Q112415.5
median14365.5
Q316317.75
95-th percentile17892.75
Maximum18287
Range21997
Interquartile range (IQR)3902.25

Descriptive statistics

Standard deviation7446.2304
Coefficient of variation (CV)0.66432358
Kurtosis-0.5736743
Mean11208.74
Median Absolute Deviation (MAD)1951.5
Skewness-1.0929403
Sum63957071
Variance55446347
MonotonicityNot monotonic
2023-05-29T16:33:32.219058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
-2087 1
 
< 0.1%
14053 1
 
< 0.1%
16009 1
 
< 0.1%
12922 1
 
< 0.1%
-2098 1
 
< 0.1%
16589 1
 
< 0.1%
13730 1
 
< 0.1%
16866 1
 
< 0.1%
-2096 1
 
< 0.1%
Other values (5696) 5696
99.8%
ValueCountFrequency (%)
-3710 1
< 0.1%
-3709 1
< 0.1%
-3708 1
< 0.1%
-3707 1
< 0.1%
-3706 1
< 0.1%
-3705 1
< 0.1%
-3701 1
< 0.1%
-3700 1
< 0.1%
-3697 1
< 0.1%
-3696 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18281 1
< 0.1%
18280 1
< 0.1%
18278 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%

lifetime
Real number (ℝ)

Distinct305
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.153
Minimum0
Maximum373
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-05-29T16:33:32.653371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1109
median238
Q3318
95-th percentile369
Maximum373
Range373
Interquartile range (IQR)209

Descriptive statistics

Standard deviation116.57112
Coefficient of variation (CV)0.53929913
Kurtosis-1.2334936
Mean216.153
Median Absolute Deviation (MAD)96
Skewness-0.29342833
Sum1233369
Variance13588.827
MonotonicityDecreasing
2023-05-29T16:33:32.835682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
373 101
 
1.8%
372 98
 
1.7%
366 88
 
1.5%
368 78
 
1.4%
365 76
 
1.3%
369 70
 
1.2%
358 66
 
1.2%
367 61
 
1.1%
371 57
 
1.0%
359 46
 
0.8%
Other values (295) 4965
87.0%
ValueCountFrequency (%)
0 4
 
0.1%
1 11
0.2%
2 7
 
0.1%
3 13
0.2%
4 18
0.3%
5 9
0.2%
7 13
0.2%
8 6
 
0.1%
9 14
0.2%
10 21
0.4%
ValueCountFrequency (%)
373 101
1.8%
372 98
1.7%
371 57
1.0%
369 70
1.2%
368 78
1.4%
367 61
1.1%
366 88
1.5%
365 76
1.3%
364 45
0.8%
362 32
 
0.6%

recency
Real number (ℝ)

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.90536
Minimum0
Maximum373
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-05-29T16:33:33.044904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q123
median71
Q3199.75
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)176.75

Descriptive statistics

Standard deviation111.57499
Coefficient of variation (CV)0.9544044
Kurtosis-0.63999466
Mean116.90536
Median Absolute Deviation (MAD)61
Skewness0.81457626
Sum667062
Variance12448.979
MonotonicityNot monotonic
2023-05-29T16:33:33.393970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 110
 
1.9%
4 105
 
1.8%
3 98
 
1.7%
2 92
 
1.6%
10 86
 
1.5%
8 82
 
1.4%
9 79
 
1.4%
17 79
 
1.4%
7 78
 
1.4%
15 67
 
1.2%
Other values (294) 4830
84.6%
ValueCountFrequency (%)
0 38
 
0.7%
1 110
1.9%
2 92
1.6%
3 98
1.7%
4 105
1.8%
5 52
0.9%
7 78
1.4%
8 82
1.4%
9 79
1.4%
10 86
1.5%
ValueCountFrequency (%)
373 23
0.4%
372 23
0.4%
371 17
0.3%
369 4
 
0.1%
368 13
0.2%
367 16
0.3%
366 15
0.3%
365 19
0.3%
364 11
0.2%
362 7
 
0.1%

avg_purchase_interval
Real number (ℝ)

Distinct1242
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.389285
Minimum0.66666667
Maximum186.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-05-29T16:33:33.600434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.66666667
5-th percentile1
Q11
median12.372414
Q353.107143
95-th percentile128
Maximum186.5
Range185.83333
Interquartile range (IQR)52.107143

Descriptive statistics

Standard deviation44.234909
Coefficient of variation (CV)1.3248235
Kurtosis1.760834
Mean33.389285
Median Absolute Deviation (MAD)11.372414
Skewness1.5287103
Sum190519.26
Variance1956.7272
MonotonicityNot monotonic
2023-05-29T16:33:33.785757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2673
46.8%
62 15
 
0.3%
30 15
 
0.3%
92 14
 
0.2%
186 14
 
0.2%
53 13
 
0.2%
16.5 12
 
0.2%
26 12
 
0.2%
90 11
 
0.2%
39 11
 
0.2%
Other values (1232) 2916
51.1%
ValueCountFrequency (%)
0.6666666667 1
 
< 0.1%
1 2673
46.8%
1.534979424 1
 
< 0.1%
1.718894009 1
 
< 0.1%
2.207100592 1
 
< 0.1%
2.25 1
 
< 0.1%
2.5 1
 
< 0.1%
2.96031746 1
 
< 0.1%
3 1
 
< 0.1%
3.127118644 1
 
< 0.1%
ValueCountFrequency (%)
186.5 11
0.2%
186 14
0.2%
185.5 5
 
0.1%
184.5 11
0.2%
184 8
0.1%
183.5 4
 
0.1%
183 7
0.1%
182.5 10
0.2%
182 4
 
0.1%
181 2
 
< 0.1%

nunique_products
Real number (ℝ)

Distinct439
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.617596
Minimum1
Maximum1786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-05-29T16:33:33.983742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q113
median36
Q384
95-th percentile241
Maximum1786
Range1785
Interquartile range (IQR)71

Descriptive statistics

Standard deviation101.64794
Coefficient of variation (CV)1.4600897
Kurtosis43.951941
Mean69.617596
Median Absolute Deviation (MAD)28
Skewness4.7072082
Sum397238
Variance10332.303
MonotonicityNot monotonic
2023-05-29T16:33:34.178115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 279
 
4.9%
2 149
 
2.6%
3 113
 
2.0%
10 101
 
1.8%
5 98
 
1.7%
9 96
 
1.7%
6 93
 
1.6%
8 93
 
1.6%
11 93
 
1.6%
4 90
 
1.6%
Other values (429) 4501
78.9%
ValueCountFrequency (%)
1 279
4.9%
2 149
2.6%
3 113
2.0%
4 90
 
1.6%
5 98
 
1.7%
6 93
 
1.6%
7 90
 
1.6%
8 93
 
1.6%
9 96
 
1.7%
10 101
 
1.8%
ValueCountFrequency (%)
1786 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
1109 1
< 0.1%
884 1
< 0.1%
817 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
720 1
< 0.1%

avg_basket_size
Real number (ℝ)

Distinct2490
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.98652
Minimum-140.5
Maximum14149
Zeros12
Zeros (%)0.2%
Negative3
Negative (%)0.1%
Memory size89.2 KiB
2023-05-29T16:33:34.388113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-140.5
5-th percentile4
Q165.406061
median133.5
Q3257
95-th percentile668.5
Maximum14149
Range14289.5
Interquartile range (IQR)191.59394

Descriptive statistics

Standard deviation419.32137
Coefficient of variation (CV)1.8637622
Kurtosis450.27512
Mean224.98652
Median Absolute Deviation (MAD)84.5
Skewness16.009513
Sum1283773.1
Variance175830.42
MonotonicityNot monotonic
2023-05-29T16:33:34.567493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 114
 
2.0%
2 70
 
1.2%
3 52
 
0.9%
4 49
 
0.9%
5 35
 
0.6%
6 29
 
0.5%
12 27
 
0.5%
72 24
 
0.4%
7 21
 
0.4%
120 20
 
0.4%
Other values (2480) 5265
92.3%
ValueCountFrequency (%)
-140.5 1
 
< 0.1%
-101 1
 
< 0.1%
-44 1
 
< 0.1%
0 12
 
0.2%
0.25 1
 
< 0.1%
0.6666666667 1
 
< 0.1%
1 114
2.0%
2 70
1.2%
3 52
0.9%
3.333333333 1
 
< 0.1%
ValueCountFrequency (%)
14149 1
< 0.1%
13956 1
< 0.1%
7824 1
< 0.1%
5963 1
< 0.1%
5197 1
< 0.1%
4300 1
< 0.1%
4280 1
< 0.1%
4136 1
< 0.1%
3206.083333 1
< 0.1%
3028 1
< 0.1%

purchase_count
Real number (ℝ)

Distinct63
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0553803
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-05-29T16:33:34.761857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile14
Maximum243
Range242
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.1309021
Coefficient of variation (CV)2.0049666
Kurtosis274.18899
Mean4.0553803
Median Absolute Deviation (MAD)1
Skewness12.593167
Sum23140
Variance66.111569
MonotonicityNot monotonic
2023-05-29T16:33:34.948944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2672
46.8%
2 812
 
14.2%
3 490
 
8.6%
4 381
 
6.7%
5 283
 
5.0%
6 195
 
3.4%
7 152
 
2.7%
8 117
 
2.1%
9 80
 
1.4%
10 73
 
1.3%
Other values (53) 451
 
7.9%
ValueCountFrequency (%)
1 2672
46.8%
2 812
 
14.2%
3 490
 
8.6%
4 381
 
6.7%
5 283
 
5.0%
6 195
 
3.4%
7 152
 
2.7%
8 117
 
2.1%
9 80
 
1.4%
10 73
 
1.3%
ValueCountFrequency (%)
243 1
< 0.1%
217 1
< 0.1%
169 1
< 0.1%
126 1
< 0.1%
118 2
< 0.1%
88 1
< 0.1%
75 1
< 0.1%
73 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%

charge_back_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58762706
Minimum0
Maximum45
Zeros4199
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-05-29T16:33:35.110569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7490544
Coefficient of variation (CV)2.9764702
Kurtosis190.73026
Mean0.58762706
Median Absolute Deviation (MAD)0
Skewness10.274232
Sum3353
Variance3.0591913
MonotonicityNot monotonic
2023-05-29T16:33:35.249332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4199
73.6%
1 852
 
14.9%
2 289
 
5.1%
3 140
 
2.5%
4 92
 
1.6%
5 37
 
0.6%
6 32
 
0.6%
7 21
 
0.4%
9 8
 
0.1%
10 5
 
0.1%
Other values (13) 31
 
0.5%
ValueCountFrequency (%)
0 4199
73.6%
1 852
 
14.9%
2 289
 
5.1%
3 140
 
2.5%
4 92
 
1.6%
5 37
 
0.6%
6 32
 
0.6%
7 21
 
0.4%
8 5
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
35 1
 
< 0.1%
27 1
 
< 0.1%
21 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
0.1%

return_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct143
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.076905605
Minimum0
Maximum0.75
Zeros4199
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-05-29T16:33:35.418147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.090909091
95-th percentile0.5
Maximum0.75
Range0.75
Interquartile range (IQR)0.090909091

Descriptive statistics

Standard deviation0.14837871
Coefficient of variation (CV)1.9293615
Kurtosis2.5147132
Mean0.076905605
Median Absolute Deviation (MAD)0
Skewness1.8858298
Sum438.82338
Variance0.022016242
MonotonicityNot monotonic
2023-05-29T16:33:35.601187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4199
73.6%
0.5 246
 
4.3%
0.3333333333 212
 
3.7%
0.25 165
 
2.9%
0.2 139
 
2.4%
0.1666666667 78
 
1.4%
0.1428571429 61
 
1.1%
0.4 55
 
1.0%
0.125 46
 
0.8%
0.2857142857 40
 
0.7%
Other values (133) 465
 
8.1%
ValueCountFrequency (%)
0 4199
73.6%
0.01369863014 1
 
< 0.1%
0.02222222222 1
 
< 0.1%
0.02272727273 1
 
< 0.1%
0.02631578947 1
 
< 0.1%
0.02857142857 1
 
< 0.1%
0.0303030303 1
 
< 0.1%
0.03225806452 1
 
< 0.1%
0.03333333333 1
 
< 0.1%
0.03448275862 1
 
< 0.1%
ValueCountFrequency (%)
0.75 1
 
< 0.1%
0.7142857143 1
 
< 0.1%
0.6666666667 19
 
0.3%
0.6 14
 
0.2%
0.5714285714 5
 
0.1%
0.5555555556 2
 
< 0.1%
0.5454545455 2
 
< 0.1%
0.5 246
4.3%
0.4736842105 1
 
< 0.1%
0.4615384615 4
 
0.1%

avg_unt_price
Real number (ℝ)

Distinct5276
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7139415
Minimum0.06
Maximum434.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.2 KiB
2023-05-29T16:33:35.797663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile1.2515888
Q12.2175
median3.0467987
Q34.25
95-th percentile6.6969669
Maximum434.65
Range434.59
Interquartile range (IQR)2.0325

Descriptive statistics

Standard deviation7.8417661
Coefficient of variation (CV)2.1114404
Kurtosis1956.3654
Mean3.7139415
Median Absolute Deviation (MAD)0.95800549
Skewness40.055514
Sum21191.75
Variance61.493296
MonotonicityNot monotonic
2023-05-29T16:33:35.976799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 26
 
0.5%
4.95 20
 
0.4%
0.85 18
 
0.3%
3.75 16
 
0.3%
2.95 15
 
0.3%
1.65 14
 
0.2%
0.42 13
 
0.2%
2.08 13
 
0.2%
12.75 12
 
0.2%
2.55 12
 
0.2%
Other values (5266) 5547
97.2%
ValueCountFrequency (%)
0.06 1
< 0.1%
0.1225 1
< 0.1%
0.17 2
< 0.1%
0.2327777778 1
< 0.1%
0.29 2
< 0.1%
0.32 1
< 0.1%
0.33 1
< 0.1%
0.355 2
< 0.1%
0.358 1
< 0.1%
0.3666666667 1
< 0.1%
ValueCountFrequency (%)
434.65 1
< 0.1%
295 1
< 0.1%
125 1
< 0.1%
110 2
< 0.1%
74.975 1
< 0.1%
66.475 1
< 0.1%
59.73333333 1
< 0.1%
54.3 1
< 0.1%
51.71 1
< 0.1%
39.95 1
< 0.1%

avg_order_value
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5467
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean511.8143
Minimum-398.43
Maximum52940.94
Zeros5
Zeros (%)0.1%
Negative8
Negative (%)0.1%
Memory size89.2 KiB
2023-05-29T16:33:36.155815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-398.43
5-th percentile12.4125
Q1140.35411
median248.225
Q3433.735
95-th percentile1774.885
Maximum52940.94
Range53339.37
Interquartile range (IQR)293.38089

Descriptive statistics

Standard deviation1354.2907
Coefficient of variation (CV)2.6460587
Kurtosis746.59792
Mean511.8143
Median Absolute Deviation (MAD)131.63667
Skewness21.758181
Sum2920412.4
Variance1834103.3
MonotonicityNot monotonic
2023-05-29T16:33:36.344754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95 9
 
0.2%
1.25 8
 
0.1%
2.95 8
 
0.1%
3.75 8
 
0.1%
4.95 8
 
0.1%
1.65 7
 
0.1%
12.75 7
 
0.1%
5.95 6
 
0.1%
7.5 6
 
0.1%
4.25 6
 
0.1%
Other values (5457) 5633
98.7%
ValueCountFrequency (%)
-398.43 1
 
< 0.1%
-47.16 1
 
< 0.1%
-31.97666667 1
 
< 0.1%
-1.136868377 × 10-131
 
< 0.1%
-5.684341886 × 10-141
 
< 0.1%
-2.842170943 × 10-141
 
< 0.1%
-1.421085472 × 10-142
 
< 0.1%
0 5
0.1%
2.842170943 × 10-141
 
< 0.1%
2.273736754 × 10-131
 
< 0.1%
ValueCountFrequency (%)
52940.94 1
< 0.1%
50653.91 1
< 0.1%
21389.6 1
< 0.1%
18745.86 1
< 0.1%
14855.53 1
< 0.1%
12681.58 1
< 0.1%
12633.67 1
< 0.1%
12172.09 1
< 0.1%
11540.34 1
< 0.1%
10661.69 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5465
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1718.8394
Minimum-796.86
Maximum278778.02
Zeros5
Zeros (%)0.1%
Negative8
Negative (%)0.1%
Memory size89.2 KiB
2023-05-29T16:33:36.526738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-796.86
5-th percentile12.46
Q1230.2875
median603.17
Q31544.6075
95-th percentile5188.815
Maximum278778.02
Range279574.88
Interquartile range (IQR)1314.32

Descriptive statistics

Standard deviation7358.9531
Coefficient of variation (CV)4.28135
Kurtosis741.04642
Mean1718.8394
Median Absolute Deviation (MAD)474.185
Skewness23.735003
Sum9807697.7
Variance54154191
MonotonicityNot monotonic
2023-05-29T16:33:36.696913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95 9
 
0.2%
4.95 8
 
0.1%
1.25 8
 
0.1%
2.95 8
 
0.1%
1.65 7
 
0.1%
12.75 7
 
0.1%
3.75 7
 
0.1%
4.25 6
 
0.1%
7.5 6
 
0.1%
5.95 6
 
0.1%
Other values (5455) 5634
98.7%
ValueCountFrequency (%)
-796.86 1
 
< 0.1%
-141.48 1
 
< 0.1%
-95.93 1
 
< 0.1%
-2.273736754 × 10-131
 
< 0.1%
-1.136868377 × 10-131
 
< 0.1%
-5.684341886 × 10-141
 
< 0.1%
-2.842170943 × 10-142
 
< 0.1%
0 5
0.1%
5.684341886 × 10-141
 
< 0.1%
4.547473509 × 10-131
 
< 0.1%
ValueCountFrequency (%)
278778.02 1
< 0.1%
259657.3 1
< 0.1%
189735.53 1
< 0.1%
133007.13 1
< 0.1%
123638.18 1
< 0.1%
114505.32 1
< 0.1%
88138.2 1
< 0.1%
65920.12 1
< 0.1%
62924.1 1
< 0.1%
59419.34 1
< 0.1%

Interactions

2023-05-29T16:33:29.417482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:08.265345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:10.121394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:12.057791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:14.066974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:16.058049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:18.134409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:20.040871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:21.828105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:23.645889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:25.748790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:27.575074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:29.553389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:08.392878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:10.263303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:12.197144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:14.210734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:16.200563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:18.275415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:20.174048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:21.960109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:23.786785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:25.880116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:27.710312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:29.712130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:08.544040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:10.423615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:12.500894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:14.378152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:16.366993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:18.438843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:20.328754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:22.124373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:23.951429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:26.036049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:27.867093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:29.870270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:08.696382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:10.586845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:12.661762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:14.541986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:16.533724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:18.602907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:20.490109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:22.290170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:24.328585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:26.194990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:28.025643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:30.030325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:08.847505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:10.750466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:12.823960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:14.705959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:16.701300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:18.767009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:20.645737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:22.454951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:24.494645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:26.404002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:28.188584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:30.190360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:08.999515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:10.914322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:12.988561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:14.871291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:16.867380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:18.930280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:20.806872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:22.619355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:24.659210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:26.559379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:28.353853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:30.420243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:09.202136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:11.079936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:13.151226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:15.035780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:17.036677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:19.090638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:20.962094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:22.773363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:24.825844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:26.712945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:28.515490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:30.567771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:09.414140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:11.284562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:13.304810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:15.188782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:17.189475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:19.258127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:21.105733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:22.917477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:24.978328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:26.856552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:28.669394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:30.770841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:09.548997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:11.436605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:13.450652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:15.432976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:17.338378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:19.409131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:21.243232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:23.054639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:25.127528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:26.994049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:28.817444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:30.945376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:09.704428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:11.603938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:13.614726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:15.600594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:17.507467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:19.579766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:21.400324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:23.213196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:25.293387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:27.150099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:28.979444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:31.090266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:09.837623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:11.749513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:13.762035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:15.747428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:17.827959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:19.727218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:21.537519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:23.350554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:25.440728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:27.285749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:29.122306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:31.243025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:09.980239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:11.903021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:13.912354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:15.901834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:17.982317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:19.883242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:21.683287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:23.495266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:25.596004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:27.431458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-29T16:33:29.269994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-29T16:33:36.859550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
customer_idlifetimerecencyavg_purchase_intervalnunique_productsavg_basket_sizepurchase_countcharge_back_countreturn_rateavg_unt_priceavg_order_valuegross_revenue
customer_id1.0000.142-0.2360.3960.0070.0250.3980.2190.214-0.233-0.0950.092
lifetime0.1421.0000.2320.3930.196-0.0570.4360.2890.2330.035-0.0220.282
recency-0.2360.2321.000-0.299-0.326-0.179-0.575-0.329-0.2690.203-0.067-0.425
avg_purchase_interval0.3960.393-0.2991.0000.2000.0360.7330.3650.398-0.149-0.0540.332
nunique_products0.0070.196-0.3260.2001.0000.6070.4300.2790.2180.0640.6190.799
avg_basket_size0.025-0.057-0.1790.0360.6071.0000.088-0.008-0.035-0.1680.8580.692
purchase_count0.3980.436-0.5750.7330.4300.0881.0000.6880.631-0.160-0.0010.625
charge_back_count0.2190.289-0.3290.3650.279-0.0080.6881.0000.977-0.050-0.0620.415
return_rate0.2140.233-0.2690.3980.218-0.0350.6310.9771.000-0.044-0.0910.340
avg_unt_price-0.2330.0350.203-0.1490.064-0.168-0.160-0.050-0.0441.0000.1970.079
avg_order_value-0.095-0.022-0.067-0.0540.6190.858-0.001-0.062-0.0910.1971.0000.723
gross_revenue0.0920.282-0.4250.3320.7990.6920.6250.4150.3400.0790.7231.000

Missing values

2023-05-29T16:33:31.470313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-29T16:33:31.775873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idlifetimerecencyavg_purchase_intervalnunique_productsavg_basket_sizepurchase_countcharge_back_countreturn_rateavg_unt_priceavg_order_valuegross_revenue
017850.0373.0372.010.65714321.048.371429351.00.0285713.960370151.1037145288.63
113047.0373.056.023.312500105.084.687500167.00.4375003.926082193.0687503089.10
212583.0373.02.021.941176114.0292.823529172.00.1176472.140474389.9611766629.34
313748.0373.095.074.60000024.087.80000050.00.0000003.996429189.650000948.25
415100.0373.0333.062.1666671.09.66666763.00.50000010.950000105.850000635.10
515291.0373.025.019.63157961.0109.105263195.00.2631584.702255239.5531584551.51
614688.0373.07.013.814815148.0119.333333276.00.2222222.131040189.1622225107.38
717809.0373.016.026.64285746.0144.000000142.00.1428572.905738381.7750005344.85
815311.0373.00.03.161017567.0319.66101711827.00.2288142.506036503.55372959419.34
916098.0373.087.053.28571434.087.57142970.00.0000004.424627286.5185712005.63
customer_idlifetimerecencyavg_purchase_intervalnunique_productsavg_basket_sizepurchase_countcharge_back_countreturn_rateavg_unt_priceavg_order_valuegross_revenue
5696-3701.01.01.01.055.01074.010.00.07.2545164839.424839.42
569713298.01.01.01.02.096.010.00.03.750000360.00360.00
569814569.01.01.01.010.079.010.00.03.920000227.39227.39
5699-3705.01.01.01.07.014.010.00.01.27857117.9017.90
5700-3706.01.01.01.02.02.010.00.01.6750003.353.35
5701-3707.01.01.01.0634.01747.010.00.04.3209465699.005699.00
5702-3708.00.00.01.0730.02010.010.00.04.1759046756.066756.06
5703-3709.00.00.01.056.0654.010.00.06.2696613217.203217.20
5704-3710.00.00.01.0217.0731.010.00.06.3643783950.723950.72
570512713.00.00.01.037.0505.010.00.02.084595794.55794.55